datalad-slurm is an extension to the DataLad package for high-performance computing (HPC), specifically slurm systems.
DataLad is a package which facilitates adherence to the FAIR research data management principles.
datalad-slurm sits on top of the main DataLad package, and it is designed to improve the DataLad workflow on HPC systems. The package is aimed at slurm systems due to the prominence of slurm in HPC settings, but in the future it may be extended to HPC systems more generally.
datalad-slurm makes it easier for users to manage their research data on HPC systems with DataLad, and also solves the following conflicts of DataLad usage in HPC systems:
- Inefficient sequential sections in highly parallel HPC jobs
- Critical race conditions between git commands in concurrent jobs
First, install the main DataLad package and its dependencies.
Then, clone this repository and install the extension with:
pip install -e .
To schedule a slurm script:
datalad slurm-schedule --output=<output_files_or_dir> <slurm_submission_command>
where <output_files_or_dir> are the expected outputs from the job, and <slurm_submission_command> is for example sbatch submit_script. Further optional command line arguments can be found in the documentation.
Multiple jobs (including array jobs) can be scheduled sequentially. They are tracked in an SQLite database. Note that any open jobs must not have conflicting outputs with previously scheduled jobs. This is so that the outputs of each slurm run can be tracked to the slurm job which generated them.
To finish (i.e. post-process) these jobs (once they are complete), simply run:
datalad slurm-finish
Alternatively, to finish a particular scheduled job, run:
datalad slurm-finish <slurm_job_id>
This will create a [DATALAD SLURM RUN] entry in the git log, analogous to a datalad run command.
datalad-slurm will flag an error for any jobs which could not be post-processed, either because they are still running, or the job failed. These are not automatically cleared from the SQLite database. The output files should first be removed or manually added in git, before running
datalad slurm-finish --close-failed-jobs
To clear the SQLite database. To inspect the current status of all open jobs (without saving anything in git), run:
datalad slurm-finish --list-open-jobs
To reschedule a previously scheduled job:
datalad slurm-reschedule <schedule_commit_hash>
where <schedule_commit_hash> is the commit hash of the previously scheduled job. There must also be a corresponding datalad slurm-finish command to the original datalad slurm-schedule, otherwise datalad slurm-reschedule will throw an error.
In the lingo of the original DataLad package, the combination of datalad slurm-schedule + datalad slurm-finish is similar to datalad run, and datalad slurm-reschedule + datalad slurm-finish is similar to datalad rerun.
An example workflow could look like this (constructed deliberately to have some failed jobs):
datalad slurm-schedule -o models/abrupt/gold/ sbatch submit_gold.slurm
datalad slurm-schedule -o models/abrupt/silver/ sbatch submit_silver.slurm
datalad slurm-schedule -o models/abrupt/bronze/ sbatch submit_bronze.slurm
datalad slurm-schedule -o models/abrupt/platinum/ sbatch submit_array_platinum.slurm
Checking the job statuses at some point while they are running:
datalad slurm-finish --list-open-jobs
The following jobs are open:
slurm-job-id slurm-job-status
10524442 COMPLETED
10524535 RUNNING
10524556 FAILED
10524620 PENDING
Later, once all the jobs have finished running:
datalad slurm-finish
add(ok): models/abrupt/gold/05_02/slurm-10524442.out (file)
add(ok): models/abrupt/gold/05_02/slurm-job-10524442.env.json (file)
add(ok): models/abrupt/gold/05_02/model_0.model.gz (file)
save(ok): . (dataset)
add(ok): models/abrupt/silver/05_02/slurm-10524535.out (file)
add(ok): models/abrupt/silver/05_02/slurm-job-10524535.env.json (file)
add(ok): models/abrupt/silver/05_02/model_0.model.gz (file)
add(ok): models/abrupt/silver/05_02/model.scaler.gz (file)
save(ok): . (dataset)
finish(impossible): [Slurm job(s) for job 10524556 are not complete.Statuses: 10524556: FAILED]
finish(impossible): [Slurm job(s) for job 10524620 are not complete.Statuses: 10524620_0: COMPLETED, 10524620_1: COMPLETED, 10524620_2: TIMEOUT]
action summary:
add (ok: 7)
finish (impossible: 2)
save (ok: 2)
To close the failed jobs:
datalad slurm-finish --close-failed-jobs
finish(ok): [Closing failed / cancelled jobs. Statuses: 10524556: FAILED]
finish(ok): [Closing failed / cancelled jobs. Statuses: 10524620_0: COMPLETED, 10524620_1: COMPLETED, 10524620_2: TIMEOUT]
action summary:
finish (ok: 2)
Note that if any sub-job of an array job fails, that whole job is treated as a failed job. The user always has the option to manually commit the successful outputs if desired.
The git history would then appear like so:
git log --oneline
a8e4aa6 (HEAD -> master) [DATALAD SLURM RUN] Slurm job 10524535: Completed
25067fe [DATALAD SLURM RUN] Slurm job 10524442: Completed
With one particular entry looking like:
commit a8e4aa62519db3b5f63243cc925ee918984bf506 (HEAD -> master)
Author: Tim Callow <tim@notmyrealemail.com>
Date: Tue Feb 18 09:31:47 2025 +0100
[DATALAD SLURM RUN] Slurm job 10524535: Completed
=== Do not change lines below ===
{
"chain": [],
"cmd": "sbatch submit_silver.slurm",
"commit_id": null,
"dsid": "61576cad-ea4f-4425-8f35-16b9955c9926",
"extra_inputs": [],
"inputs": [],
"outputs": [
"models/abrupt/silver",
"models/abrupt/silver/05_02/slurm-10524535.out",
"models/abrupt/silver/05_02/slurm-job-10524535.env.json"
],
"pwd": ".",
"slurm_job_id": 10524535,
"slurm_outputs": [
"models/abrupt/silver/05_02/slurm-10524535.out",
"models/abrupt/silver/05_02/slurm-job-10524535.env.json"
]
}
^^^ Do not change lines above ^^^
The datalad-slurm extension is still in the very early stages of development. We welcome contributors and testers of the package. Please document any issues on GitHub and we will try to resolve them.
See CONTRIBUTING.md if you are interested in internals or contributing to the project.